Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 datasets:
-- ElKulako/StockTwits-crypto
+- ElKulako/stocktwits-crypto
 
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@@ -9,18 +9,30 @@ CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of
 ## Classification Training
 The model was trained on the following labels: "Bearish" : 0, "Neutral": 1, "Bullish": 2
 
-CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, bootstrapped from [ElKulako/StockTwits-crypto](https://huggingface.co/datasets/ElKulako/StockTwits-crypto). 
+CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, bootstrapped from [ElKulako/stocktwits-crypto](https://huggingface.co/datasets/ElKulako/stocktwits-crypto). 
 
 CryptoBERT was trained with a max sequence length of 128. Technically, it can handle sequences of up to 514 tokens, however, going beyond 128 is not recommended.
 
 # Classification Example
+```python
+>>> from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
+>>> from datasets import load_dataset
+>>> dataset_name = "ElKulako/stocktwits-crypto"
+>>> dataset = load_dataset(dataset_name)
+>>> model_name = "ElKulako/cryptobert"
+>>> tokenizer_ = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+>>> model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3)
+>>> pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, batch_size=64, max_length=64, truncation=True, padding = 'max_length')
+>>> preds = pipe(df_posts)
 
 
+```
+
 ## Training Corpus
 CryptoBERT was trained on 3.2M social media posts regarding various cryptocurrencies. Only non-duplicate posts of length above 4 words were considered. The following communities were used as sources for our corpora:
 
 
-(1) StockTwits - 1.875M posts about the top 100 cryptos by trading volume. Posts were collected from the 1st of November 2021 to the 16th of June 2022. [ElKulako/StockTwits-crypto](https://huggingface.co/datasets/ElKulako/StockTwits-crypto)
+(1) StockTwits - 1.875M posts about the top 100 cryptos by trading volume. Posts were collected from the 1st of November 2021 to the 16th of June 2022. [ElKulako/stocktwits-crypto](https://huggingface.co/datasets/ElKulako/stocktwits-crypto)
 
 (2) Telegram - 664K posts from top 5 telegram groups: [Binance](https://t.me/binanceexchange), [Bittrex](https://t.me/BittrexGlobalEnglish), [huobi global](https://t.me/huobiglobalofficial), [Kucoin](https://t.me/Kucoin_Exchange), [OKEx](https://t.me/OKExOfficial_English). 
 Data from 16.11.2020 to 30.01.2021. Courtesy of [Anton](https://www.kaggle.com/datasets/aagghh/crypto-telegram-groups).